Bibliographic record
Abstract
Public acceptance of autonomous and connected cars Bobby CottamThe self-driving car, long a ubiquitous staple of science fiction, is finally becoming a reality.The requisite technology is under development.There are issues of liability, but they will be relatively small matters to resolve if a burgeoning market and an available profit margin provides the right incentive.The one true obstacle left to the reality of the self-driving car is that of public acceptance.This might prove to be a very real obstacle, as many people are wary, if not out-rightly fearful, of being on the road with autonomous vehicles.And if the public will not accept these vehicles, then none of the other considerations really mattertechnological advancements or the resolution of legal issues will become a moot point.We can hope to sway public opinion towards wider acceptance by, first and foremost, answering the primary challenges raised against this new technology.The first and loudest concern is: will it be safe?Before it can be released, much less fully accepted, the technology must be exceptionally safe.But here we cannot allow the perfect to be the enemy of the good.In 2013, according to the National Highway Traffic Safety Administration, the United States had over 5 million reported accidents with over 1 million injuries and more than 30 000 fatalities.Progress must be seen not as eliminating these occurrences, but rather in reducing them.The metric for success must be in reducing fatal accidentsby 50%, 75%, or 90%not in reducing the accident record to zero.It is a statistically shown reality that young and inexperienced drivers are some of the most dangerous; if we could bring the quality of autonomous vehicles merely to the driving ability of an attentive experienced middle aged adult, that would be a happy improvement that most parents would gladly embraceboth for the safety of their children and for the likely reduction of their insurance premium.A second common objection comes from those that find driving intrinsically enjoyable.To many a car represents freedom and funit is the sentiment stemming from the quintessential car commercial, a scene where a lone motorist speeds along some twisty mountain road, hair blowing in the breeze.However, here we must admit that not all driving is equally enjoyable.The most ardent motorist will likely agree that a daily commute during Chicago rush hour is a chore.The same is true of exceedingly long tripsmost people would be more than ok with leaving Los Angeles at eight in the evening, watching a movie, having a drink, and then waking up in Denver in the morning.This would allow them to combine the convenience and economy of driving while not having to give up productive time that the drive would traditionally require; for family trips it would certainly make it easier to entertain the children.Autonomous vehicles need not eliminate driving as a hobby.If unwelcome, difficult, and tedious driving was turned over to autonomous vehicles, more traditional driving could remain as a purely recreational activity.Driving could become an activity like hunting or sailingsomething that used to be done out of necessity that now remains for the thrill.It is also important to note that this objection, that of enjoying driving, appears to reflect a substantial generational bias.The newest generation of commuters is far less inclined to enjoy driving or to even want to drivethey are getting their licenses later, and are comfortable with the train or an Uber.This tech-heavy generation chases the newest tablet far more than the nicest car, and is inclined to select methods of travel that keep their hands and attention free for other tasks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".